Improving power output wind turbine in micro-grids assisted virtual wind speed prediction

Wind energy is an alternative form of energy easily obtainable in the landscape. However, the main challenge is to extract electrical power from varying wind speeds. Wind energy can be a significant production resource for power electronics technologies, converters, and electrical generators. Due to their dependence on wind speed, the output power from wind turbines experiences severe fluctuations with the change in wind speed, and ripples increase the output power from the wind turbine. Therefore, the engineers’ critical research prediction will smooth these extraction fluctuations. Several speed prediction methods have been used to reduce the changes in the output power of wind turbines. One of these wind speed prediction methods is a fast energy storage system that can be charged and discharged in seconds. Applying wind speed prediction to overcome the slowness of the wind source will be the primary approach considered in this article. Also, a wind turbine with a nominal power of 50 kW and an ultra-capacitor storage system are determined, and these sources are made in MATLAB/SIMULINK softwareIn this study, the control signal for adjusting the turbine pitch angle is derived from both actual and predicted data. The signal from actual data undergoes a multiplication by 0.8, while the signal from predicted data is multiplied by 0.2. This approach serves two purposes: firstly, it helps prevent overshooting of turbine power at the initial stages, ensuring a smoother transition. Secondly, it aids in maintaining a consistent power output of 50 kW during subsequent moments. By combining actual and predicted data in this weighted manner, the control system achieves a balanced response, effectively managing turbine power dynamics. Finally, the results show that utilizing wind speed prediction to improve output wind turbine in Micro-grids (MGs) will reduce fluctuations in the wind source's output power and the ultra-capacitor storage.